Methods for Improving Image Quality and Reducing Data Load of NIR Hyperspectral Images

Sensors

Ferenc Firtha¹  András Fekete²  Tímea Kaszab²  B໛orka Gillay²  M󩷪 Nogula-Nagy²  Zoltán Kovผs² 

[1] id="af1-sensors-08-03287">Corvinus University of Budapest, Faculty of Food Science, Department of Physics and Control, Somlói út 14-16, H-1118 Budapest, Hunga

关键词: Hyperspectral;    noise;    data-extraction;    carrot;    moisture-content;   

DOI  :  10.3390/s8053287

来源: mdpi

【 摘 要 】

Near Infrared Hyperspectral Imaging (NIRHSI) is an emerging technology platform that integrates conventional imaging and spectroscopy to attain both spatial and spectral information from an object. Two important problems in NIRHSI are those of data load and unserviceable pixels in the NIR sensor. Hyperspectral imaging experiments generate large amounts of data (typically > 50 MB per image), which tend to overwhelm the memory capacity of conventional computer systems. This inhibits the utilisation of NIRHSI for routine online industrial application. In general, approximately 1% of pixels in NIR detectors are unserviceable or ‘dead’, containing no useful information. While this percentage of pixels is insignificant for single wavelength imaging, the problem is amplified in NIRHSI, where > 100 wavelength images are typically acquired. This paper describes an approach for reducing the data load of hyperspectral experiments by using sample-specific vector-to-scalar operators for real time feature extraction and a systematic procedure for compensating for ‘dead’ pixels in the NIR sensor. The feasibility of this approach was tested for prediction of moisture content in carrot tissue.

【 授权许可】

CC BY   
© 2008 by the authors; licensee Molecular Diversity Preservation International, Basel, Switzerland.